diff --git a/examples/cross_spectra.ipynb b/examples/cross_spectra.ipynb new file mode 100644 index 0000000..f90323a --- /dev/null +++ b/examples/cross_spectra.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reproducing the results of Wheler and Kiladis 99" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "from matplotlib import pyplot as plt\n", + "from pywk99.spectrum.spectrum import get_cross_spectrum\n", + "from pywk99.timeseries import remove_seasonal_cycle\n", + "from pywk99.timeseries import remove_linear_trend\n", + "from pywk99.waves import plot_dispersion_relations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.4.4'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pywk99\n", + "pywk99.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# run data/download.ipynb to download the example data\n", + "slp = xr.open_mfdataset(\"data/slp.19*.nc\").slp.compute()\n", + "olr = xr.open_dataset(\"data/olr.2xdaily.1979-2022.nc\").sel(\n", + " time=slice(\"1979\", \"1996\")\n", + " ).resample({\"time\": \"1D\"}).mean().olr.compute()\n", + "variables = xr.Dataset({\"olr\": olr, \"slp\": slp})\n", + "variables = variables.sortby(\"lat\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "variables_normalized = remove_linear_trend(variables)\n", + "variables_normalized = remove_seasonal_cycle(variables_normalized)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "window_length = \"92D\"\n", + "overlap_length = \"60D\"\n", + "data_frequency = \"1D\"\n", + "taper_alpha = 0.1\n", + "symmetric_spectrum = get_cross_spectrum(\n", + " variables_normalized,\n", + " \"symmetric\",\n", + " data_frequency,\n", + " window_length,\n", + " overlap_length,\n", + " taper_alpha=taper_alpha,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "COHERENCE_THRESHOLD = 0.05\n", + "coh2 = symmetric_spectrum.coherence_squared\n", + "coh2 = coh2.where(coh2 > COHERENCE_THRESHOLD, drop=True)\n", + "u = xr.apply_ufunc(np.real, symmetric_spectrum.cross) / np.abs(symmetric_spectrum.cross)\n", + "v = xr.apply_ufunc(np.imag, symmetric_spectrum.cross) / np.abs(symmetric_spectrum.cross)\n", + "u = u.where(coh2 > COHERENCE_THRESHOLD, drop=True)\n", + "v = v.where(coh2 > COHERENCE_THRESHOLD, drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1 , 1, figsize=(5, 4))\n", + "coh2.plot.contourf(ax=ax)\n", + "stride = 3\n", + "ax.quiver(u.wavenumber[::stride],\n", + " u.frequency[::stride],\n", + " u[::stride,::stride],\n", + " v[::stride,::stride],\n", + " angles=\"uv\", pivot='mid', units='x', width=0.15)\n", + "plot_dispersion_relations(\"symmetric\", ax, w_max=0.5)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pywk99", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pywk99/__init__.py b/pywk99/__init__.py index 8acf24e..83891f8 100644 --- a/pywk99/__init__.py +++ b/pywk99/__init__.py @@ -3,6 +3,10 @@ Changelog --------- +Jul 18, 2025: Version 0.4.4 + - Adding coherence to the cross spectra function. + - Fixing bugs in the cross spectra. + Apr 8, 2025: Version 0.4.3 - Adding support for healpix @@ -70,4 +74,4 @@ - Several bugs where detected and corrected with the tests. """ -__version__ = "0.4.3" +__version__ = "0.4.4" diff --git a/pywk99/filter/window.py b/pywk99/filter/window.py index 7b103c5..b1d6e57 100644 --- a/pywk99/filter/window.py +++ b/pywk99/filter/window.py @@ -12,7 +12,7 @@ class FilterPoint(Point): """See shapely.geometry.Point.""" -@dataclass(frozen=True) +@dataclass class FilterWindow: name: str polygon: Union[Polygon, MultiPolygon] diff --git a/pywk99/spectrum/__init__.py b/pywk99/spectrum/__init__.py index f680acf..c68c553 100644 --- a/pywk99/spectrum/__init__.py +++ b/pywk99/spectrum/__init__.py @@ -8,3 +8,4 @@ from pywk99.spectrum.background import get_background_spectrum from pywk99.spectrum.plot import plot_spectrum from pywk99.spectrum.plot import plot_spectrum_peaks + diff --git a/pywk99/spectrum/_spectrum.py b/pywk99/spectrum/_spectrum.py index 595e937..e81cc9f 100644 --- a/pywk99/spectrum/_spectrum.py +++ b/pywk99/spectrum/_spectrum.py @@ -110,7 +110,7 @@ def get_spectrum(spc_quantity: str, ] number_of_spectrums = len(wk_spectrums) wk_spectrum = (sum(wk_spectrums) / number_of_spectrums).sum("lat") - wk_spectrum = wk_spectrum[wk_spectrum.frequency > 0] + wk_spectrum = wk_spectrum.where(wk_spectrum.frequency > 0, drop=True) wk_spectrum = wk_spectrum.sortby(["frequency", "wavenumber"]) return wk_spectrum @@ -178,9 +178,17 @@ def _compute_hayashi_cross_spectrum(variables: xr.Dataset) -> xr.DataArray: n_time = len(variables.time) n_lon = len(variables.lon) varlist = list(variables.keys()) - variable1_fft = fourier_transform(variables[varlist[0]]) / (n_time * n_lon) - variable2_fft = fourier_transform(variables[varlist[1]]) / (n_time * n_lon) - cross_spectrum = variable1_fft * np.conj(variable2_fft) + variable1_name = varlist[0] + variable2_name = varlist[1] + variable1_fft = fourier_transform(variables[variable1_name]) + variable2_fft = fourier_transform(variables[variable2_name]) + spectrum_1 = np.abs(variable1_fft)**2 / (n_time * n_lon)**2 + spectrum_2 = np.abs(variable2_fft)**2 / (n_time * n_lon)**2 + cross_1_2 = variable1_fft * np.conj(variable2_fft) / (n_time * n_lon)**2 + spectrum_1.name = f"spectra1" + spectrum_2.name = f"spectra2" + cross_1_2.name = f"cross" + cross_spectrum = xr.merge([spectrum_1, spectrum_2, cross_1_2]) return cross_spectrum diff --git a/pywk99/spectrum/spectrum.py b/pywk99/spectrum/spectrum.py index 5228455..2d0b6e4 100644 --- a/pywk99/spectrum/spectrum.py +++ b/pywk99/spectrum/spectrum.py @@ -92,9 +92,9 @@ def get_cross_spectrum( taper_alpha: Optional[float] = 0.5, grid_type: str = "latlon", grid_dict: Optional[dict] = None, -) -> xr.DataArray: +) -> xr.Dataset: """ - Get the Wheeler and Kiladis 1999 cross spectrum of two variables. + Get the Wheeler and Kiladis 1999 cross spectrum. See pywk99.spectrum.get_spectrum for argument documentation. """ @@ -111,9 +111,18 @@ def get_cross_spectrum( grid_type, grid_dict ) + cross_spectrum["coherence_squared"] = _coherence_squared(cross_spectrum) return cross_spectrum +def _coherence_squared(cross_spectrum: xr.Dataset) -> xr.Dataset: + """Compute the squared coherence a cross spectrum.""" + sxy2 = np.abs(cross_spectrum.cross)**2 + sxx = np.abs(cross_spectrum.spectra1) + syy = np.abs(cross_spectrum.spectra2) + return sxy2 / (sxx * syy) + + def get_co_spectrum( variable: xr.Dataset, component_type: str, @@ -144,7 +153,7 @@ def get_co_spectrum( grid_type, grid_dict ) - co_spectrum = np.real(cross_spectrum) + co_spectrum = np.real(cross_spectrum.cross) return co_spectrum @@ -178,5 +187,6 @@ def get_quadrature_spectrum( grid_type, grid_dict ) - quadrature_spectrum = np.imag(cross_spectrum) + quadrature_spectrum = np.imag(cross_spectrum.cross) return quadrature_spectrum + diff --git a/tests/test_spectrum_coh2.py b/tests/test_spectrum_coh2.py new file mode 100644 index 0000000..789ca16 --- /dev/null +++ b/tests/test_spectrum_coh2.py @@ -0,0 +1,32 @@ +"""Test power spectrum for time-lon-lat fields.""" + +import numpy as np +import pytest +import xarray as xr + +from pywk99.spectrum.spectrum import get_cross_spectrum + + +@pytest.fixture +def variables(): + olr1 = xr.open_dataarray("tests/olr.test.nc").transpose( + "time", "lon", "lat" + ) + olr2 = olr1.copy() + variables = xr.Dataset({"olr1": olr1, "olr2": olr2}) + variables = variables.sortby(["lat"]) + return variables + + +@pytest.fixture(params=["symmetric", "asymmetric"]) +def cross_spectrum(variables, request): + component_type = request.param + cross_spectrum = get_cross_spectrum( + variables, component_type, window_length="30D", overlap_length="10D" + ) + return cross_spectrum + + +def test_coherence_squared_is_one_for_the_same_field(cross_spectrum) -> None: + coh2 = cross_spectrum.coherence_squared + assert np.all(np.ravel(coh2.values) == pytest.approx(1.0))